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never been higher, so much so that it can overshadow the
real applications and actual outcomes various companies
are working on. But larger than hyped up life promises
may have an eclipsing affect over the actual, realistic
benefits it provides to almost any organization, in a wide
variety of industries that are generating large volumes of
data.
AI in healthcare today:
For healthcare decision makers, governments, investors
and innovators, and the European Union itself AI is now in
high demand. An increasing number of governments have
set out aspirations for AI in healthcare, in countries as
diverse as Finland, Germany, the United Kingdom, Israel,
China, and the United States, and many are investing
heavily in AI-related research.
What impact will AI have on the healthcare workforce?
The MGI has looked into how automation and artificial
intelligence (AI) are expected to alter the future of work. It
believes that automation, if it hasn’t already, will infiltrate
its way into most employment across all sectors.
However, different sectors will respond differently to the
requirement of AI, and healthcare is one of the industries
with the lowest overall potential for automation—only
35% of time spent is theoretically automatable, with the
percentage varying by profession. The possibility of
automation is not the same as the likelihood of adoption.
What has to change in order for AI to be introduced
and scaled up in healthcare?
The progress that the health industry has made so far, with
the help of Artificial Intelligence has been significant.
However, the road to building a future where AI
contributes consistently and extensively towards achieving
worldwide benefits in healthcare, will definitely be
challenging.
No doubt, in the healthcare industry, AI isn’t necessarily
an absolute problem solver and inculcating it does come
with a few price tags. In a recent research, which also took
into consideration the views of certain stakeholders and
frontline workers, a set of issues pertaining to the same
have been shed light upon:
Collaboration to deliver high-quality AI in healthcare:
In the research done, one of the issues that was highlighted
was the quality of AI performance, particularly
emphasising on bad use case selection, AI design and
simplicity, algorithm quality and performance, and the
robustness and completeness of relevant but not visible
data.
Major challenges to addressing quality issues early on and
adopting solutions at scale were highlighted as a lack of
multidisciplinary development and early involvement of
healthcare workers, as well as limited iteration by joint AI
and healthcare teams.
Only 14% of start-up executives thought healthcare
professionals' input was critical in the early design phase,
according to the survey, while the role of the private sector
in the aggregation and analysing of data, creating an
efficient and secure data base, or even aiding upskill
healthcare staff, was seen as unimportant by healthcare
professionals.
Giving education and skill development a second
thought.
We've already discussed the need of digital skills, which
are currently lacking in most practitioners' toolkits.
Leaders in healthcare who are well-versed in both biology
and data science will be required for AI in healthcare.
Recent efforts have been made to train students in the
science of medicine, biology, and informatics through joint
degrees, albeit this is less common in Europe.
To elaborate more, all practitioners need to prioritise
important and basic skill sets of basic digital literacy,
genomics foundations, AI and machine learning, as well as
critical-thinking abilities and the honing of a continuouslearning
mindset.
Along with improving clinical training, healthcare
organisations must consider their current workforce and
provide ongoing learning opportunities, while practitioners
must have the time and motivation to do so.
- Arran Calvert
26 | May 2021 | www.insightscare.com